Automatically update your AI chatbot's knowledge base

Breaks new documents into chunks and folds them into your chatbot's searchable knowledge base.

How the work actually flows

A straight line. Runs once per one per text chunk.

Pattern: Multiple Instances without Synchronization (12)

flowchart TD trig>"new document submitted"]:::trig s0["split document into chunks"]:::task s1[["convert chunks to embeddings"]]:::mi s2[("store chunks in vector database")]:::store trig --> s0 s0 -->|"one per one per text chunk"| s1 s1 --> s2 out[/"updated searchable knowledge base"/]:::out pay{{"chatbot answers stay accurate"}}:::pay s2 --> out out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff
Starts itA stepRuns once per itemA record or sheetResultPayoff
Build size
Advanced

A larger build with multiple systems, AI reasoning, and custom rules.

Business functions
Knowledge Base & RAG
Connects
OpenAIPinecone

The problem it solves

When you have new documents for your knowledge base, they need to be processed correctly before your chatbot can actually use them to answer questions. Doing that formatting and uploading by hand every time you have new material is tedious and easy to get wrong.

Who it fits

Teams maintaining a chatbot's knowledge base who need new documents processed correctly every time.

How it works

  1. A document is sent in to be added to the knowledge base
  2. The text is split into smaller chunks sized for search
  3. Each chunk is converted into a format the database can search by meaning
  4. The chunks are stored in the vector database, ready for the chatbot to use
What you get

Documents folded into your chatbot's knowledge automatically

New documents get automatically prepared and added to your chatbot's knowledge base, so it can search and use them right away.

What you get

An updated, searchable knowledge base ready for the chatbot to draw on.

What you need

An OpenAI API key and a Pinecone account.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

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